![]() ![]() In some cases, an object can be so distinct from nearby distractors that the time required to find the object is independent of the number of distractors (e.g., the “pop-out” of color). The properties of attention have been explored using visual search tasks. Attention can also be directed toward nonspatial properties, such as color or event, but even in such cases, spatial localization remains important ( Bichot et al. 2000) and psychophysical methods ( Bahcall and Kowler 1999 Caputo and Guerra 1998) indicates that attention imposes a ring of inhibition around an attended item, indicating a limitation to the window analogy. 2001) and in monkey lateral geniculate nucleus (LGN) and V1 ( Tootell et al. Recent work using imaging methods in human V1 ( Smith et al. 1991) that is moved either by eye movements or by “covert” processes that occur without eye movements ( Posner et al. In a simplified view, attention can be considered a window ( Broadbent and Broadbent 1990 Campbell 1985 Nakayama 1991 Van Essen et al. 2001 Lamme and Roelfsema 2000).Ī second important property of information flow in cortex is that it is controlled by attentional processes. Recordings from different levels of the cortical hierarchy during recognition show only minor latency differences (∼10 ms), suggesting that during the subsequent recognition process there can be interaction of B-U, T-D, and lateral information flow ( Hupe et al. Thus contextual expectancies may affect the visual system even before the stimulus arrives. For example, in “semantic priming,” recognition of words in a category is enhanced if subjects know the category ( Lorch et al. 1999) and provide a way for high-level information to affect perception ( Ress et al. These T-D connections can strongly affect neuronal function ( Cauller and Kulics 1991 Lee et al. Anatomical studies have demonstrated the existence of massive connections from higher level areas back to lower level areas ( Rockland and Pandya 1981 Salin and Bullier 1995). However, there is also a top-down (T-D) flow of information the function of which is less clear. This bottom-up (B-U) information flow has been extensively studied. Processing in the first cortical region, V1, detects features (oriented contrast gradients), whereas processing at higher levels detects more complex patterns by combining inputs from lower-levels ( Reid 2001 Tanaka 1996). 2000 Felleman and Van Essen 1991 Lerner et al. The visual recognition process is performed by a hierarchy of cortical areas in the ventral stream ( Barone et al. More generally the model provides a physiologically plausible view of how bi-directional signal flow in cortex guides attention to produce efficient recognition. This model accounts for the findings that recognition time depends logarithmically on set size, recognition time is reduced when context reduces the number of possible targets, the time to classify a nonword decreases when its approximation to English decreases, and in high level cortex, the firing of neurons tuned to an object increases progressively as its recognition occurs. Because covert attention can be moved every 20–30 ms, word recognition could be as fast as determined experimentally (<200 ms of cortical processing). We show that when 950 words are stored in long-term memory, recognition occurs after an average of 4.9 cycles. Recognition occurs after several such cycles when all but one word has been excluded. ![]() Bottom-up processing of this feature excludes words that do not contain it and leads to T-D recomputation of feature probabilities. ![]() This information is then used to guide the window of attention to information-rich features (e.g., a feature that is present in the visual image but has lowest probability). Simple connectivity rules allow a parallel top-down (T-D) computation of the relative probability of each feature at each location given the set of active words. At the start of the recognition process, nodes representing all contextually possible words are active. To make this concrete, we modeled the process of visual word recognition by hierarchical cortical areas representing features, letters, and words. Here we explore how the cortex can move this window and integrate the sampled information. The entry of signals into higher areas involves the serial sampling of information within a movable window of attention. Visual recognition is achieved by a hierarchy of bidirectionally connected cortical areas.
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